Advancing Landcover Classification with Deep Learning and Satellite Imagery

Accurate land cover classification is crucial for effective land management and environmental monitoring. This study utilizes the Deep Globe Land Cover Classification Dataset to develop a deep learning model for seven land cover classes: agriculture, urban, rangeland, water, forest, barren, and unknown. The distinguishing dataset, which comprises high-resolution satellite imagery, was leveraged to train a Convolutional Neural Network (CNN) designed to capture and classify intricate patterns across diverse land types. The model's architecture integrates advanced techniques in deep learning to enhance feature extraction and classification accuracy. Our approach achieved significant improvements in classifying complex land cover types, with high overall accuracy and precision across all categories. This paper establishes the efficacy of deep learning methodologies in large-scale land cover classification tasks and provides a robust framework for upcoming applications in environmental monitoring, urban planning and resource management.